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Deadlocked
From Wikipedia, the free encyclopedia. In concurrent programming, a deadlock is a situation in which two or more competing actions are each waiting for the other to finish, and thus neither ever does. In a transactional database, a deadlock happens when two processes each within its own transaction updates two rows of information but in the opposite order. For example, process A updates row 1 then row 2 in the exact timeframe that process B updates row 2 then row 1. Process A can't finish updating row 2 until process B is finished, but process B cannot finish updating row 1 until process A is finished. No matter how much time is allowed to pass, this situation will never resolve itself and because of this database management systems will typically kill the transaction of the process that has done the least amount of work. In an operating system, a deadlock is a situation which occurs when a process or thread enters a waiting state because a resource requested is being held by another waiting process, which in turn is waiting for another resource held by another waiting process. If a process is unable to change its state indefinitely because the resources requested by it are being used by another waiting process, then the system is said to be in a deadlock.1 Deadlock is a common problem in multiprocessing systems, parallel computing and distributed systems, where software and hardware locks are used to handle shared resources and implement process synchronization.2 In telecommunication systems, deadlocks occur mainly due to lost or corrupt signals instead of resource contention.3 Contents hide 1 Examples 2 Necessary conditions 3 Avoiding database deadlocks 4 Deadlock handling 4.1 Ignoring deadlock 4.2 Detection 4.3 Prevention 4.4 Avoidance 5 Livelock 6 Distributed deadlock 7 See also 8 References 9 Further reading 10 External links Examplesedit Any deadlock situation can be compared to the classic "chicken or egg" problem.4 It can also be considered a paradoxical "Catch-22" situation.5 A real world example would be an illogical statute passed by the Kansas legislature in the early 20th century, which stated:16 “ When two trains approach each other at a crossing, both shall come to a full stop and neither shall start up again until the other has gone. ” A simple computer-based example is as follows. Suppose a computer has three CD drives and three processes. Each of the three processes holds one of the drives. If each process now requests another drive, the three processes will be in a deadlock. Each process will be waiting for the "CD drive released" event, which can be only caused by one of the other waiting processes. Thus, it results in a circular chain. Moving onto the source code level, a deadlock can occur even in the case of a single thread and one resource (protected by a mutex). Assume there is a function func1() which does some work on the resource, locking the mutex at the beginning and releasing it after it's done. Next, somebody creates a different function func2() following that pattern on the same resource (lock, do work, release) but decides to include a call to func1() to delegate a part of the job. What will happen is the mutex will be locked once when entering func2() and then again at the call to func1(), resulting in a deadlock if the mutex is not reentrant (i.e. the plain "fast mutex" variety). Necessary conditionsedit A deadlockers situation can arise if all of the following conditions hold simultaneously in a system:1 1.Mutual Exclusion: At least one resource must be held in a non-shareable mode.1 Only one process can use the resource at any given instant of time. 2.Hold and Wait or Resource Holding: A process is currently holding at least one resource and requesting additional resources which are being held by other processes. 3.No Preemption: a resource can be released only voluntarily by the process holding it. 4.Circular Wait: A process must be waiting for a resource which is being held by another process, which in turn is waiting for the first process to release the resource. In general, there is a set of waiting processes, P = {P1, P2, ..., PN}, such that P1 is waiting for a resource held by P2, P2 is waiting for a resource held by P3 and so on until PN is waiting for a resource held by P1.17 These four conditions are known as the Coffman conditions from their first description in a 1971 article by Edward G. Coffman, Jr.7 Unfulfillment of any of these conditions is enough to preclude a deadlock from occurring. Avoiding database deadlocksedit This section is written like a manual or guidebook. Please help rewrite this section from a descriptive, neutral point of view, and remove advice or instruction. (August 2013) This section may contain content that is repetitive or redundant of text elsewhere in the article. Please help improve it by merging similar text or removing repeated statements. (September 2014) An effective way to avoid database deadlocks is to follow this approach from the Oracle Locking Survival Guide: Application developers can eliminate all risk of enqueue deadlocks by ensuring that transactions requiring multiple resources always lock them in the same order.8 This single sentence needs much explanation to understand the recommended solution. First it highlights the fact that processes must be inside a transaction for deadlocks to happen. Note that some database systems can be configured to cascade deletes which creates an implicit transaction which then can cause deadlocks. Also some DBMS vendors offer row-level locking, a type of record locking which greatly reduces the chance of deadlocks, as opposed to page level locking, which creates many times more locks. Second, by "multiple resources" this means more than one row in one or more tables. An example of locking in the same order would be to process all INSERTS first, all UPDATES second, and all DELETES last and within processing each of these handle all parent table changes before children table changes; and process table changes in the same order such as alphabetically or ordered by an ID or account number. Third, eliminating all risk of deadlocks is difficult to achieve as the DBMS has automatic lock escalation features that raise row level locks into page locks which can be escalated to table locks. Although the risk or chance of experiencing a deadlock will not go to zero as deadlocks tend to happen more on large, high-volume, complex systems, it can be greatly reduced and when required the software can be enhanced to retry transactions when a deadlock is detected. Fourth, deadlocks can result in data loss if the software is not developed to use transactions on every interaction with a DBMS and the data loss is difficult to locate and creates unexpected errors and problems. Deadlocks are a challenging problem to correct as they result in data loss, are difficult to isolate, create unexpected problems, and are time consuming to fix. Modifying every section of software code in a large system that access the database to always lock resources in the same order when the order is inconsistent takes significant resources and testing to implement. Deadlock handlingedit Most current operating systems cannot prevent a deadlock from occurring.1 When a deadlock occurs, different operating systems respond to them in different non-standard manners. Most approaches work by preventing one of the four Coffman conditions from occurring, especially the fourth one.9 Major approaches are as follows. Ignoring deadlockedit In this approach, it is assumed that a deadlock will never occur. This is also an application of the Ostrich algorithm.910 This approach was initially used by MINIX and UNIX.7 This is used when the time intervals between occurrences of deadlocks are large and the data loss incurred each time is tolerable. Detectionedit Under deadlock detection, deadlocks are allowed to occur. Then the state of the system is examined to detect that a deadlock has occurred and subsequently it is corrected. An algorithm is employed that tracks resource allocation and process states, it rolls back and restarts one or more of the processes in order to remove the detected deadlock. Detecting a deadlock that has already occurred is easily possible since the resources that each process has locked and/or currently requested are known to the resource scheduler of the operating system.10 Deadlock detection techniques include, but are not limited to, model checking. This approach constructs a finite state-model on which it performs a progress analysis and finds all possible terminal sets in the model. These then each represent a deadlock. After a deadlock is detected, it can be corrected by using one of the following methods: 1.Process Termination: One or more processes involved in the deadlock may be aborted. We can choose to abort all processes involved in the deadlock. This ensures that deadlock is resolved with certainty and speed. But the expense is high as partial computations will be lost. Or, we can choose to abort one process at a time until the deadlock is resolved. This approach has high overheads because after each abort an algorithm must determine whether the system is still in deadlock. Several factors must be considered while choosing a candidate for termination, such as priority and age of the process. 2.Resource Preemption: Resources allocated to various processes may be successively preempted and allocated to other processes until the deadlock is broken. Preventionedit Main article: Deadlock prevention algorithms Deadlock prevention works by preventing one of the four Coffman conditions from occurring. ##Removing the mutual exclusion condition means that no process will have exclusive access to a resource. This proves impossible for resources that cannot be spooled. But even with spooled resources, deadlock could still occur. Algorithms that avoid mutual exclusion are called non-blocking synchronization algorithms. ##The hold and wait or resource holding conditions may be removed by requiring processes to request all the resources they will need before starting up (or before embarking upon a particular set of operations). This advance knowledge is frequently difficult to satisfy and, in any case, is an inefficient use of resources. Another way is to require processes to request resources only when it has none. Thus, first they must release all their currently held resources before requesting all the resources they will need from scratch. This too is often impractical. It is so because resources may be allocated and remain unused for long periods. Also, a process requiring a popular resource may have to wait indefinitely, as such a resource may always be allocated to some process, resulting in resource starvation.1 (These algorithms, such as serializing tokens, are known as the all-or-none algorithms.) ##The no preemption condition may also be difficult or impossible to avoid as a process has to be able to have a resource for a certain amount of time, or the processing outcome may be inconsistent or thrashing may occur. However, inability to enforce preemption may interfere with a priority algorithm. Preemption of a "locked out" resource generally implies a rollback, and is to be avoided, since it is very costly in overhead. Algorithms that allow preemption include lock-free and wait-free algorithms and optimistic concurrency control.This condition may be removed as follows : If a process holding some resources and requests for some another resource(s) which cannot be immediately allocated to it, then by releasing all the currently being held resources of that process. ##The final condition is the circular wait condition. Approaches that avoid circular waits include disabling interrupts during critical sections and using a hierarchy to determine a partial ordering of resources. If no obvious hierarchy exists, even the memory address of resources has been used to determine ordering and resources are requested in the increasing order of the enumeration.1 Dijkstra's solution can also be used. Avoidanceedit This section may contain content that is repetitive or redundant of text elsewhere in the article. Please help improve it by merging similar text or removing repeated statements. (September 2014) Deadlock can be avoided if certain information about processes are available to the operating system before allocation of resources, such as which resources a process will consume in its lifetime. For every resource request, the system sees whether granting the request will mean that the system will enter an unsafe state, meaning a state that could result in deadlock. The system then only grants requests that will lead to safe states.1 In order for the system to be able to determine whether the next state will be safe or unsafe, it must know in advance at any time: ##resources currently available ##resources currently allocated to each process ##resources that will be required and released by these processes in the future It is possible for a process to be in an unsafe state but for this not to result in a deadlock. The notion of safe/unsafe states only refers to the ability of the system to enter a deadlock state or not. For example, if a process requests A which would result in an unsafe state, but releases B which would prevent circular wait, then the state is unsafe but the system is not in deadlock. One known algorithm that is used for deadlock avoidance is the Banker's algorithm, which requires resource usage limit to be known in advance.1 However, for many systems it is impossible to know in advance what every process will request. This means that deadlock avoidance is often impossible. Two other algorithms are Wait/Die and Wound/Wait, each of which uses a symmetry-breaking technique. In both these algorithms there exists an older process (O) and a younger process (Y). Process age can be determined by a timestamp at process creation time. Smaller timestamps are older processes, while larger timestamps represent younger processes. Wait/Die Wound/Wait O needs a resource held by Y O waits Y dies Y needs a resource held by O Y dies Y waits Another way to avoid deadlock is to avoid blocking, for example by using Non-blocking synchronization or Read-copy-update. Livelockedit A livelock is similar to a deadlock, except that the states of the processes involved in the livelock constantly change with regard to one another, none progressing. This term was defined formally at some time during the 1970s ‒ an early sighting in the published literature is in Babich's 1979 article on program correctness.11 Livelock is a special case of resource starvation; the general definition only states that a specific process is not progressing.12 A real-world example of livelock occurs when two people meet in a narrow corridor, and each tries to be polite by moving aside to let the other pass, but they end up swaying from side to side without making any progress because they both repeatedly move the same way at the same time. Livelock is a risk with some algorithms that detect and recover from deadlock. If more than one process takes action, the deadlock detection algorithm can be repeatedly triggered. This can be avoided by ensuring that only one process (chosen arbitrarily or by priority) takes action.13 Distributed deadlockedit Distributed deadlocks can occur in distributed systems when distributed transactions or concurrency control is being used. Distributed deadlocks can be detected either by constructing a global wait-for graph from local wait-for graphs at a deadlock detector or by a distributed algorithm like edge chasing. Phantom deadlocks are deadlocks that are falsely detected in a distributed system due to system internal delays but don't actually exist. For example, if a process releases a resource R1 and issues a request for R2, and the first message is lost or delayed, a coordinator (detector of deadlocks) could falsely conclude a deadlock (if the request for R2 while having R1 would cause a deadlock). See alsoedit ##Banker's algorithm ##Catch 22 ##Dining philosophers problem ##File locking ##Gridlock (in vehicular traffic) ##Hang ##Impasse ##Infinite loop ##Linearizability ##Model checker can be used to formally verify that a system will never enter a deadlock. ##Ostrich algorithm ##Priority inversion ##Race condition ##Sleeping barber problem ##Stalemate ##Readers-writer lock ##Synchronization Referencesedit 1.^ Jump up to: a b c d e f g h i j Silberschatz, Abraham (2006). Operating System Principles (7 ed.). Wiley-India. p. 237. ISBN 9788126509621. Retrieved 29 January 2012. 2.Jump up ^ Padua, David (2011). Encyclopedia of Parallel Computing. Springer. p. 524. ISBN 9780387097657. Retrieved 28 January 2012. 3.Jump up ^ Schneider, G. Michael (2009). Invitation to Computer Science. Cengage Learning. p. 271. ISBN 0324788592. Retrieved 28 January 2012. 4.Jump up ^ Rolling, Andrew (2009). Andrew Rollings and Ernest Adams on game design. New Riders. p. 421. ISBN 9781592730018. Retrieved 28 January 2012. 5.Jump up ^ Oaks, Scott (2004). Java Threads. O'Reilly. p. 64. ISBN 9780596007829. Retrieved 28 January 2012. 6.Jump up ^ A Treasury of Railroad Folklore, B.A. Botkin & A.F. Harlow, p. 381 7.^ Jump up to: a b c Shibu, K. (2009). Intro To Embedded Systems (1st ed.). Tata McGraw-Hill Education. p. 446. ISBN 9780070145894. Retrieved 28 January 2012. 8.Jump up ^ "Oracle Locking Survival Guide". Archived from the original on 15 January 2008. 9.^ Jump up to: a b Stuart, Brian L. (2008). Principles of operating systems (1st ed.). Cengage Learning. p. 446. Retrieved 28 January 2012. 10.^ Jump up to: a b Tanenbaum, Andrew S. (1995). Distributed Operating Systems (1st ed.). Pearson Education. p. 117. Retrieved 28 January 2012. 11.Jump up ^ Babich, A.F. (1979). "Proving Total Correctness of Parallel Programs". IEEE Transactions on Software Engineering SE–5 (6): 558–574. doi:10.1109/tse.1979.230192. 12.Jump up ^ Anderson, James H.; Yong-jik Kim (2001). "Shared-memory mutual exclusion: Major research trends since 1986". 13.Jump up ^ Zöbel, Dieter (October 1983). "The Deadlock problem: a classifying bibliography". ACM SIGOPS Operating Systems Review 17 (4): 6–15. doi:10.1145/850752.850753. ISSN 0163-5980. Further readingedit ##Kaveh, Nima; Emmerich, Wolfgang. "Deadlock Detection in Distributed Object Systems" (PDF). London: University College London. ##Bensalem, Saddek; Fernandez, Jean-Claude; Havelund, Klaus; Mounier, Laurent (2006). "Confirmation of deadlock potentials detected by runtime analysis". Proceedings of the 2006 workshop on Parallel and distributed systems: Testing and debugging (ACM): 41–50. doi:10.1145/1147403.1147412. ISBN 1595934146. ##Coffman, Edward G., Jr.; Elphick, Michael J.; Shoshani, Arie (1971). "System Deadlocks" (PDF). ACM Computing Surveys 3 (2): 67–78. doi:10.1145/356586.356588. ##Mogul, Jeffrey C.; Ramakrishnan, K. K. (1997). "Eliminating receive livelock in an interrupt-driven kernel". ACM Transactions on Computer Systems 15 (3): 217–252. doi:10.1145/263326.263335. ISSN 0734-2071. ##Havender, James W. (1968). "Avoiding deadlock in multitasking systems". IBM Systems Journal 7 (2): 74. doi:10.1147/sj.72.0074. ##Holliday, JoAnne L.; El Abbadi, Amr. "Distributed Deadlock Detection". Encyclopedia of Distributed Computing (Kluwer Academic Publishers). ##Knapp, Edgar (1987). "Deadlock detection in distributed databases". ACM Computing Surveys 19 (4): 303–328. doi:10.1145/45075.46163. ISSN 0360-0300. ##Ling, Yibei; Chen, Shigang; Chiang, Jason (2006). "On Optimal Deadlock Detection Scheduling". IEEE Transactions on Computers 55 (9): 1178–1187. doi:10.1109/tc.2006.151. External linksedit ##"Advanced Synchronization in Java Threads" by Scott Oaks and Henry Wong ##Deadlock Detection Agents ##DeadLock at the Portland Pattern Repository ##Etymology of "Deadlock" Category:Levels